Long only 1/n portfolio

import pandas as pd
pd.options.plotting.backend = "plotly"

import yfinance as yf

from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index

# Get rid of findfont: Font family 'Arial' not found.
# when running a remote notebook on Jupyter Server on Ubuntu Linux server
import logging
logging.getLogger("matplotlib.font_manager").setLevel(logging.ERROR)

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 1
----> 1 import pandas as pd
      2 pd.options.plotting.backend = "plotly"
      4 import yfinance as yf

ModuleNotFoundError: No module named 'pandas'

data = yf.download(tickers = "SPY AAPL GOOG MSFT",  # list of tickers
                   period = "10y",                   # time period
                   interval = "1d",                 # trading interval
                   prepost = False,                 # download pre/post market hours data?
                   repair = True)                   # repair obvious price errors e.g. 100x?

[*********************100%***********************]  4 of 4 completed

prices = data["Adj Close"]

capital = 1e6

b = builder(prices=prices, initial_cash=capital)

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    b[time[-1]] = 0.25 * state.nav / state.prices

portfolio = b.build()
portfolio.profit.cumsum().plot()

portfolio.nav.plot()

## Rebalancing

Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. 
There are two approaches to deal with this problem in cvxsimulator. 

* Resample the existing daily portfolio (helpful to see effect of your hesitated trading)
* Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)

### Resample an existing portfolio

portfolio_resampled = portfolio.resample(rule="M")

frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame

	original	monthly
Date
2013-05-30	1.000000e+06	1.000000e+06
2013-05-31	9.945921e+05	9.945921e+05
2013-06-03	1.000400e+06	1.000391e+06
2013-06-04	9.917402e+05	9.917309e+05
2013-06-05	9.846364e+05	9.846127e+05
...	...	...
2023-05-23	7.505451e+06	7.493060e+06
2023-05-24	7.461416e+06	7.447306e+06
2023-05-25	7.603349e+06	7.590982e+06
2023-05-26	7.711937e+06	7.698989e+06
2023-05-30	7.735463e+06	7.721169e+06

2518 rows × 2 columns

print(portfolio_resampled.stocks)

                    AAPL          GOOG         MSFT          SPY
Date                                                            
2013-05-30  17854.390520  11527.266005  8564.268967  1814.033248
2013-05-31  17854.390520  11527.266005  8564.268967  1814.033248
2013-06-03  17895.614489  11573.477982  8432.882983  1831.100869
2013-06-04  17895.614489  11573.477982  8432.882983  1831.100869
2013-06-05  17895.614489  11573.477982  8432.882983  1831.100869
...                  ...           ...          ...          ...
2023-05-23  10590.586692  16651.905094  5882.633509  4316.566726
2023-05-24  10590.586692  16651.905094  5882.633509  4316.566726
2023-05-25  10590.586692  16651.905094  5882.633509  4316.566726
2023-05-26  10590.586692  16651.905094  5882.633509  4316.566726
2023-05-30  10590.586692  16651.905094  5882.633509  4316.566726

[2518 rows x 4 columns]

# almost hard to see that difference between the original and resampled portfolio
frame.plot()

# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()

Trade only days in predefined grid

b = builder(prices=prices, initial_cash=capital)

# define a grid
grid = resample_index(prices.index, rule="M")

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    if time[-1] in grid:
        b[time[-1]] = 0.25 * state.nav / state.prices
    else:
        # forward fill an existing position
        b[time[-1]] = b[time[-2]]
        
portfolio = b.build()

portfolio.nav.plot()

# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()

Why not resampling the prices?

I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.